Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations

Taesung Lee, Benjamin Edwards, Ian Molloy +1

Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been p…